Examples Overview

Practical examples across optimization, machine learning, algorithms, and circuits

Examples

Practical examples demonstrating the Dynex SDK across different problem domains. From simple BQM sampling to production-grade quantum machine learning, these examples cover the full spectrum of what Dynex can compute.

Prerequisites

pip install dynex dimod numpy

For ML examples:

pip install torch scikit-learn

For circuit examples:

pip install pennylane qiskit pennylane-qiskit

Basic Examples

Getting started with the fundamental SDK workflow:

Simple BQM Sampling

Build and sample a Binary Quadratic Model on CPU and QPU

BQM Usage

Constructing BQMs with dimod, PyQUBO, and named variables

Algorithm Examples

Classic quantum algorithms implemented on the Dynex platform:

Grover's Algorithm

Integer factorization via quantum amplitude amplification

Shor's Algorithm

Period-finding for efficient integer factorization

Optimization Algorithms

MaxCut, graph partitioning, job sequencing, and more

Machine Learning Examples

Quantum-enhanced ML algorithms with PyTorch and scikit-learn integration:

ML Overview

QSVM, QPCA, QNN, QBM, and feature selection

QSVM

Quantum Support Vector Machine

QRBM / QBM

Quantum Restricted Boltzmann Machine

Neuromorphic Torch Layers

Hybrid quantum-classical PyTorch models

Industry Applications

Real-world applications across industries:

Domain	Examples
Finance	Portfolio optimization, collaborative filtering
Pharma / Health	Protein folding, RNA folding, molecule screening
Automotive	Traffic optimization, EV charging placement, CFD
Logistics	Aircraft loading, job sequencing, multi-vehicle routing
Aerospace	Satellite scheduling
Computer Vision	Image classification (Q-RBM), image super-resolution (Q-SISR)

All notebooks

Browse the complete notebook collection on GitHub: github.com/Dynex-Development/awesome-dynex